What this script does:
- proceeds from the output of preprocessing script.
- takes as input data_processed_step1.csv, and
data_model_prediction.csv - plots Sorting and Causal Task
rdms, gets correlations between empirical and models RDM, gets within
subject correlations.
- outputs data_freesort_mins_causals.csv for use in study 2
script for item selection.
the tris show a mixture of what you are causing and what is being caused. the more top right you are, the more you’re showing what you cause, and the lower you are, the more you’re showing being caused top half of items: your the cause mostly, middle item: equal likelihood, bottom half: your ethe effect mostly
## mind → action: mean distance = 0.323 [95% CI: 0.309, 0.337]
## action → mind: mean distance = 0.415 [95% CI: 0.399, 0.431]
## action → action: mean distance = 0.611 [95% CI: 0.592, 0.630]
## body → body: mean distance = 0.381 [95% CI: 0.362, 0.400]
## body → action: mean distance = 0.442 [95% CI: 0.424, 0.460]
## action → body: mean distance = 0.427 [95% CI: 0.410, 0.445]
## perception → cognition: mean distance = 0.149 [95% CI: 0.130, 0.167]
## cognition → perception: mean distance = 0.477 [95% CI: 0.442, 0.512]
## seeing → sick: mean distance = 0.390 [95% CI: 0.300, 0.481]
## hearing → sick: mean distance = 0.606 [95% CI: 0.514, 0.697]
## [1] 0.03372421
##
## One Sample t-test
##
## data: subject_correlations
## t = 3.9823, df = 49, p-value = 0.0002258
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.01670612 0.05074230
## sample estimates:
## mean of x
## 0.03372421
seem fine, so no need for perm test
##
## Shapiro-Wilk normality test
##
## data: subject_correlations
## W = 0.95551, p-value = 0.05768
## $upper_bound
## [1] 0.2596685
##
## $lower_bound
## [1] 0.2595976
## $upper_tri_upper_bound
## [1] 0.4409539
##
## $upper_tri_lower_bound
## [1] 0.4413385
##
## $lower_tri_upper_bound
## [1] 0.4124777
##
## $lower_tri_lower_bound
## [1] 0.4130283
## # A tibble: 8 × 5
## method correlation_type mean_mean_correlation t_value p_value
## <fct> <chr> <dbl> <dbl> <dbl>
## 1 Sorting Task 2 Category 0.144 5.99 2.39e- 7
## 2 Sorting Task 3 Category 0.254 9.76 4.51e-13
## 3 Sorting Task 6 Category 0.194 11.1 5.77e-15
## 4 Sorting Task Cosine Similarity 0.130 7.95 2.29e-10
## 5 Causal Task 2 Category -0.0823 -4.96 8.90e- 6
## 6 Causal Task 3 Category -0.0357 -2.93 5.10e- 3
## 7 Causal Task 6 Category -0.0569 -6.20 1.16e- 7
## 8 Causal Task Cosine Similarity -0.0797 -7.49 1.14e- 9
Here we are geenerating freesort minus causal distances to itemB (i.e. the target item). These will be used to select items for study 2 in the study 2 script.
## [1] "Corr(causal rdm Causal Task first, causal rdm Sorting Task first): 0.951633021558085"
## [1] "Corr(freesort rdm Causal Task first, freesort rdm Sorting Task first): 0.861761299764209"
## [1] "correlation difference: 0.0898717217938758"
## [1] -0.05419336
## [1] "Corr(causal rdm Causal Task first, freesort rdm Causal Task first): -0.0541933616487223"
## [1] "Corr(causal rdm Sorting Task first, freesort rdm Sorting Task first): 0.0304316889389261"
## [1] 0.08462505
## [1] "Correlation difference: 0.0846250505876484"
## [1] "Z-score: 0.207441401412369"
## [1] "P-value: 0.83566515206997"